IDENTIFYING PREDICTORS OF RESILIENCE IN SINGLE-ARM STUDIES OF PRE–POST CHANGE

Abstract With advancing age, older adults are at a greater risk of experiencing significant stressors. While frailty is the increased vulnerability to stressors, resilience is the ability to respond well to a major stressor. An important goal of geriatric research is to identify factors which influence resilience to stressors. Studies of resilience in older adults are typically conducted using a single-arm design where everyone experiences the stressor. In such designs, resilience is typically quantified as the degree of recovery in physical/cognitive/psychological functions after the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to mathematical coupling and regression-to-the-mean. Correction is necessary for eliminating the bias and drawing valid inferences regarding the effect of covariates and baseline status on pre-post change. We present a simple method to correct this bias. We extend the method to include covariates. Our approach considers a counterfactual control group and involves sensitivity analyses to evaluate different settings of control group parameters. We illustrate the method using a large, registry of older adults (N=7,239) who underwent total knee replacement (TKR). We demonstrate how external data can be utilized to constrain the sensitivity analysis. Our analysis indicates that baseline (pre-stressor) function was not strongly linked to recovery after TKR. Among the covariates, only age had a consistent effect on post-stressor recovery. A main takeaway of our work is that studies of resilience should consider, either directly or indirectly, the use of an appropriate control group.

shorter and this shift in goals contributes to enhanced well-being.However, studies using the future time perspective (FTP) scale have yielded mixed findings regarding the association between time horizons and well-being.The present study aims to explore alternative constructs of time horizons to better understand well-being across the life span.In this preliminary study, a sample of 101 adults (age M = 46.99,SD = 19.20;33 males, 66 females, and 2 indicated other gender identity) filled out an online questionnaire regarding time horizons and well-being.Exploratory and confirmatory factor analyses resulted in a new time horizon construct -time preoccupation, which captures the worries and fixations associated with future preparation.Serial mediation modeling shows the trend that people experience reduced future preoccupation with age, which contributes to more time savoring, and thus better well-being outcomes.Together, our findings suggest a promising role of future preoccupation in people's well-being across the life span.These findings have important implications for future interventions that aim to improve younger adults' well-being.

AGE-RELATED DIFFERENCES IN DAILY EXPERIENCES OF HAPPINESS: THE ROLE OF THINKING ABOUT THE FUTURE
Yoonseok Choi 1 , Jennifer Lay 2 , Minjie Lu 3 , Da Jiang 4 , Helene Fung 5 , Peter Graf 1 , and Christiane Hoppmann 1 , 1. University of British Columbia,Vancouver,British Columbia,Canada,2. University of Exeter,Exeter,England,United Kingdom,3. Beijing Normal University at Zhuhai,Zhuhai,Guangdong,China (People's Republic),4. Education University of Hong Kong,Hong Kong,Hong Kong,5. The Chinese University of Hong Kong,Hong Kong,Hong Kong We examined the role of future time perspective (thinking about the future) in shaping age-related differences in timevarying experiences of happiness.Older adults' experience of happiness is more strongly associated with low-arousal than with high-arousal positive affect.Low-arousal positive affective states may be conducive to engaging in meaningful social interactions with close others (e.g., listening, adjusting to others) and therefore serve key socio-emotional goals that are prioritized when future time is perceived as limited.We hypothesized that thinking less about the future would be related to stronger associations between happiness and low-arousal positive states in older than younger adults.We used daily life assessments from 258 participants (M = 48.4years; 68% female; 77% Asian; 73% post-secondary education), which comprised older (M = 63.4 years) and younger (M = 20.1 years) adult samples collected at two locations (Vancouver, Canada; Hong Kong).Participants reported on their momentary affective states and thinking about the future (0-100 scales) up to 30 times over 10 days.Results replicate previous findings by showing that momentary happiness was more strongly associated with momentary calmness and more weakly associated with momentary excitement among older as compared to younger adults.Younger adults reported thinking more about the future than older adults.Thinking less about the future was related to stronger happiness-calmness and weaker happiness-excitement associations in daily life for older participants, only; for younger participants, it was associated with weaker happinesscalmness associations.Age and future thinking-related contours of happiness are discussed in the context of emotional aging theories.

METHODOLOGICAL CHALLENGES AND OPPORTUNITIES FOR STUDYING RESILIENCE
Chair: Ravi Varadhan Co-Chair: Karen Bandeen-Roche Discussant: George Kuchel Many older adults experience a major stressor at some point in their lives.The ability to recover and live well after a major stressor is known as resilience.An important goal of geriatric research is to identify factors which influence resilience to stressors, and ultimately, to devise strategies to enhance the resilience of older adults.In order to accomplish these worthy aims, several methodological challenges need to be addressed.Methodologies for studying resilience in clinical settings are only recently being developed.Investigators at Duke, Johns Hopkins, and Radboud Universities are studying cohorts of older individuals who are undergoing invasive clinical procedures using longitudinal designs.These studies collect a vast array of information on each participant including socio-demographics, health status, physical, cognitive, psychological, and biological functions.Also, large amount of experimental data is collected on the dynamics of select physiological systems as probes of resilience ("deep phenotyping").Deep phenotyping leads to several challenges in the conduct and analysis of the study including selective participation (selection bias), informatively missing data, characterizing resilient responses based upon trajectories of multidimensional measures before and after stressor, modeling dynamical systems experimental data, and quantification of resilience capacity and its determinants.In this symposium, we will discuss some of the critical methodological challenges of studying resilience and present approaches to address them.At the end of this symposium, the audience members should understand the importance of designs and analytic challenges in studying resilience, and become knowledgeable on the approaches to overcome methodological challenges.
Abstract citation ID: igad104.1823With advancing age, older adults are at a greater risk of experiencing significant stressors.While frailty is the increased vulnerability to stressors, resilience is the ability to respond well to a major stressor.An important goal of geriatric research is to identify factors which influence resilience to stressors.Studies of resilience in older adults are typically conducted using a single-arm design where everyone experiences the stressor.In such designs, resilience is typically quantified as the degree of recovery in physical/cognitive/psychological functions after the stressor.The simplistic approach of regressing change versus baseline yields biased estimates due to mathematical coupling and regression-to-the-mean.Correction is necessary for eliminating the bias and drawing valid inferences regarding the effect of covariates and baseline status on pre-post change.We present a simple method to correct this bias.We extend the method to include covariates.Our approach considers a counterfactual control group and involves sensitivity analyses to evaluate different settings of control group parameters.We illustrate the method using a large, registry of older adults (N=7,239) who underwent total knee replacement (TKR).We demonstrate how external data can be utilized to constrain the sensitivity analysis.Our analysis indicates that baseline (pre-stressor) function was not strongly linked to recovery after TKR.Among the covariates, only age had a consistent effect on post-stressor recovery.A main takeaway of our work is that studies of resilience should consider, either directly or indirectly, the use of an appropriate control group.

IDENTIFYING PREDICTORS OF RESILIENCE IN SINGLE-ARM STUDIES OF PRE-POST CHANGE
Abstract citation ID: igad104.1824

LATENT VARIABLE MODELING FOR BIOLOGICALLY INFORMED PROGNOSIS IN STUDIES OF PHYSICAL RESILIENCE
Karen Bandeen-Roche 1 , Qianli Xue 2 , and Ravi Varadhan 3 , 1. Johns Hopkins University, Baltimore, Maryland, United States, 2. Johns Hopkins, Baltimore, Maryland, United States, 3. Johns Hopkins University School of Medicine, Baltimore, Maryland, United States Physical resilience -rebound in relevant functioning and biomarkers following a health stressor -is hypothesized to be rooted in the level of fitness of stress-response physiology defining one's "physiological resilience capacity" (PRC).This physiology forms a dynamical system comprising specific modules (the individual stress response systems and their underlying components) and their dynamic interactions with each other via feedback and other protocols.Such a system can be modeled using differential equations whose parameters may then define the PRC.We do not yet know how to measure these parameters directly, however.Rather, they are conceptually defined "constructs" which must be inferred using indirect measures-ideally, stimulus response data probing multiple aspects of the relevant physiology.Latent variable models are ideally suited to this setting.Two challenges for their application in studies of resilience are presented: (1) Integrating specific scientific knowledge on the dynamical systems in modeling the co-distribution of the indirect measures.(2) Synergizing such models' advantages for construct measurement with advantages of machine learning approaches to optimize accuracy for predicting resilient outcomes by inferred PRC.Challenges and their proposed solutions are illustrated using multifaceted stimulus response data being collected in an ongoing investigation on the physiological basis of resilience to clinical stressors in older adults, the Study of Physical Resilience in agING.The work aims to produce physiologically rooted measures providing effective risk predictors for older adults facing impending stressors as well as intervention candidates by which to promote PRC in the longer term.

APPROACHES TO QUANTITATIVELY EXAMINE RESILIENCE IN OLD AGE
Rene Melis 1 , Almar Kok 2 , and Martijn Huisman 2 , 1. Radboud University Medical Center,Nijmegen,Gelderland,Netherlands,2. Amsterdam University Medical Center,Amsterdam,Netherlands Resilience is a complex system's ability to maintain or recover in function following a perturbation.The concept is generic and can be applied to any complex system going from a cell, a person, to their support networks.By applying a resilience approach, we can better understand why people respond differently to a similar exposure.While most people have an intuitive understanding of what resilience is, it's definition and application is not straightforward.Therefore, it is important to carefully define the stressor, system and (functional) outcome(s) and how it is studied using e.g., the TransNIH resilience framework.Quantitative examination of resilience can focus on predictors, mechanisms, trajectories and/or outcomes and each aspect requires a different analytical approach.This talk will review different quantitative approaches to examine resilience in the context of aging.These approaches can be categorized based on the statistical methods that are used to operationalize resilience: the effect modification approach to study how hypothesized resilience factors modify the outcome following perturbation, scale construction in the psychometric approach, comparison of characteristics between groups based on predefined prospective resilience responses in the a priori approach, datadriven subgroup identification based on resilience outcome (trajectories) in the clustering approach, analyzing predictors of adversity-outcome residual values in the residual approach, and analyzing stressor-response patterns in intensive longitudinal data to better understand resilience mechanisms.The approaches are not mutually exclusive.Researchers may choose to combine multiple approaches and may analyze the same data using multiple approaches to compare the findings between them.One important component of the Resilience paradigm is the degree of initial change and rapidity of recovery after a stressor.Resilience is determined by both the degree of response, if any, to an insult and, subsequently, the process of return to the initial state.Modeling the recovery process requires use of longitudinal analysis which can incorporate the underlying non-linear trajectory of change, and the multiple systems impacted by the stressor.We will show the models we have employed for one common stressor, surgery.First, we will define and show findings on the Expected Predicted Differential (ERD), the difference between predicted and observed recovery.Second, we will show multivariate trajectories of recovery to demonstrate methods to (1) define